14. Iris Flower Classificaton Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as pyplot\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('datasets/Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 6)\n"
     ]
    }
   ],
   "source": [
    "#Dimensions of the Data\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Peek at the Data\n",
    "dataset.head(20)\n",
    "dataset = dataset.drop('Id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     150.000000    150.000000     150.000000    150.000000\n",
       "mean        5.843333      3.054000       3.758667      1.198667\n",
       "std         0.828066      0.433594       1.764420      0.763161\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.350000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Class Distribution\n",
    "dataset.groupby('Species').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SepalLengthCm       AxesSubplot(0.125,0.536818;0.352273x0.343182)\n",
       "SepalWidthCm     AxesSubplot(0.547727,0.536818;0.352273x0.343182)\n",
       "PetalLengthCm          AxesSubplot(0.125,0.125;0.352273x0.343182)\n",
       "PetalWidthCm        AxesSubplot(0.547727,0.125;0.352273x0.343182)\n",
       "dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualization\n",
    "#Univariate Plots\n",
    "dataset.plot(kind = 'box', subplots=True, layout=(2,2), sharex= False, sharey= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B746320>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B76AE10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B79A390>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B7BE908>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Multivariate Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B8AF780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B906C50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B93A588>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B9627B8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B989A20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B9B2C88>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B8FCB70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B9F6898>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21B9F68D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BA49D30>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BA70F98>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BAA4240>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BACA4A8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BAF2710>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BB1D978>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A21BB44BE0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_matrix(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Evaluate Some Algorithms\n",
    "array = dataset.values\n",
    "X = array[:, 0:4]\n",
    "Y = array[:,4]\n",
    "validation_size = 0.20\n",
    "seed = 7\n",
    "X_train, X_validation, Y_train, Y_validation = train_test_split(X,Y, test_size = validation_size, random_state = seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR: 0.966667 (0.040825)\n",
      "LDA: 0.975000 (0.038188)\n",
      "KNN: 0.983333 (0.033333)\n",
      "CART: 0.975000 (0.038188)\n",
      "NB: 0.975000 (0.053359)\n",
      "SVM: 0.991667 (0.025000)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n",
      "c:\\users\\dell\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "models.append(('LR', LogisticRegression()))\n",
    "models.append(('LDA', LinearDiscriminantAnalysis()))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier()))\n",
    "models.append(('NB', GaussianNB()))\n",
    "models.append(('SVM', SVC()))\n",
    "\n",
    "results = []\n",
    "names= []\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=seed)\n",
    "    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring= 'accuracy')\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare Algorithms\n",
    "from matplotlib import *\n",
    "\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9\n",
      "[[ 7  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  2  9]]\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00         7\n",
      "Iris-versicolor       0.85      0.92      0.88        12\n",
      " Iris-virginica       0.90      0.82      0.86        11\n",
      "\n",
      "       accuracy                           0.90        30\n",
      "      macro avg       0.92      0.91      0.91        30\n",
      "   weighted avg       0.90      0.90      0.90        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, Y_train)\n",
    "predictions = knn.predict(X_validation)\n",
    "print(accuracy_score(Y_validation, predictions))\n",
    "print(confusion_matrix(Y_validation, predictions))\n",
    "print(classification_report(Y_validation, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}